IJRIT International Journal of Research in Information Technology, Volume 2, Issue 5, May 2014, Pg: 290-293

International Journal of Research in Information Technology (IJRIT) www.ijrit.com

ISSN 2001-5569

Survey on Face Recognition Using Laplacian faces Chinmay Gadgil1, Kunal Kawale, Ajinkya Bhuruk3 and Mohanish Khunte4 1

Student, Pune University, Computer Department, K J College Of Engineering and Management Research Pune, Maharashtra, India [email protected]

2

Student, Pune University, Computer Department, K J College Of Engineering and Management Research Pune, Maharashtra, India [email protected]

3

Student, Pune University, Computer Department, K J College Of Engineering and Management Research Pune, Maharashtra, India [email protected]

4

Student, Pune University, Computer Department, K J College Of Engineering and Management Research Pune, Maharashtra, India [email protected]

Abstract The face recognition is quite interesting subject if we see in terms of security. A system can recognize and catch criminals and terrorists in a crowd. The proponents of large-scale face recognition feel that it is a necessary evil to make our country safer. It could benefit the visually impaired and allow them to interact more easily with the environment. Also, a computer vision-based authentication system could be put in place to allow computer access or access to a specific room using face recognition. Another possible application would be to integrate this technology into an artificial intelligence system for more realistic interaction with humans. We propose an appearance-based face recognition method called the Laplacianface approach. By using Locality Preserving Projections (LPP), the face images are mapped into a face subspace for analysis. Different from Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) which effectively see only the Euclidean structure of face space, LPP finds an embedding that preserves local information, and obtains a face subspace that best detects the essential face manifold structure. The Laplacian faces are the optimal linear approximations to the eigen functions of the Laplace Beltrami operator on the face manifold. In this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. Keywords: PCA, LDA, LPP, Security

1. Introduction A smart environment is one that is able to identify people, interpret their actions, and react appropriately. Thus, one of the most important building blocks of smart environments is a person identification system. Face recognition devices are ideal for such systems, since they have recently become fast, cheap, unobtrusive, and, when combined with voice-recognition, are very robust against changes in the environment. Moreover, since humans primarily recognize each other by their faces and voices, they feel comfortable interacting with an environment that does the same. Chinmay Gadgil,IJRIT

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 5, May 2014, Pg: 290-293

Facial recognition systems are built on computer programs that analyze images of human faces for the purpose of identifying them. The programs take a facial image, measure characteristics such as the distance between the eyes, the length of the nose, and the angle of the jaw, and create a unique file called a "template." Using templates, the software then compares that image with another image and produces a score that measures how similar the images are to each other. Typical sources of images for use in facial recognition include video camera signals and pre-existing photos such as those in driver's license databases. Facial recognition systems are computer-based security systems that are able to automatically detect and identify human faces. These systems depend on a recognition algorithm, such as eigenface or the hidden Markov model. The first step for a facial recognition system is to recognize a human face and extract it fro the rest of the scene. Next, the system measures nodal points on the face, such as the distance between the eyes, the shape of the cheekbones and other distinguishable features. These nodal points are then compared to the nodal points computed from a database of pictures in order to find a match. Obviously, such a system is limited based on the angle of the face captured and the lighting conditions present. New technologies are currently in development to create three-dimensional models of a person's face based on a digital photograph in order to create more nodal points for comparison. However, such technology is inherently susceptible to error given that the computer is extrapolating a three-dimensional model from a two-dimensional photograph. Principle Component Analysis is an eigenvector method designed to model linear variation in high-dimensional data. PCA performs dimensionality reduction by projecting the original n-dimensional data onto the k << n -dimensional linear subspace spanned by the leading eigenvectors of the data’s covariance matrix. Its goal is to find a set of mutually orthogonal basis functions that capture the directions of maximum variance in the data and for which the coefficients are pairwise decorrelated. For linearly embedded manifolds, PCA is guaranteed to discover the dimensionality of the manifold and produces a compact representation.

Eigenfaces: ”Eigenfaces for Recognition” seeks to implement a system capable of efficient, simple, and accurate face recognition in a constrained environment (such as a household or an office). The system does not depend on 3-D models or intuitive knowledge of the structure of the face (eyes, nose, mouth). Classification is instead performed using a linear combination of characteristic features (eigenfaces). The motivation behind Eigenfaces is that the previous work ignores the question of which features are important for classification, and which are not. Eigenfaces seeks to answer this by using principal component analysis of the images of the faces. This analysis reduces the dimensionality of the training set, leaving only those features that are critical for face recognition. The system is initialized by first acquiring the training set (ideally a number of examples of each subject with varied lighting and expression). Eigenvectors and eigenvalues are computed on the covariance matrix of the training images. The M highest eigenvectors are kept. Finally, the known individuals are projected into the face space space, and their weights are stored. This process is repeated as necessary.

Eigenfaces And Fisherfaces:- Eigenfaces and Fisherfaces are used for face recognition purpose. Face recognition using Eigenfaces is efficient than that of Fisherfaces. These two methods are used regularly for the detection and recognition purpose. Eigenfaces use PCA method for the facial recognition and detection purpose. Fisherfaces use LDA(Linear Discriminant Analysis) method for the face recognition purpose.

2. PCA AND LDA methods:PCA(Principal Component Analysis):- PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. Principal component analysis (PCA) involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 5, May 2014, Pg: 290-293

data as possible, and each succeeding component accounts for as much of the remaining variability as possible. LDA(Linear Discriminant Analysis):- Linear discriminant analysis (LDA) and the related Fisher's linear discriminant are methods used in statistics, pattern recognition and machine learning to find a linear combination of features which characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification. LDA is closely related to ANOVA (analysis of variance) and regression analysis, which also attempt to express one dependent variable as a linear combination of other features or measurements. However, ANOVA uses categorical independent variables and a continuous dependent variable, whereas discriminant analysis has continuous independent variables and a categorical dependent variable (i.e. the class label).[3] Logistic regression and probit regression are more similar to LDA, as they also explain a categorical variable by the values of continuous independent variables. These other methods are preferable in applications where it is not reasonable to assume that the independent variables are normally distributed, which is a fundamental assumption of the LDA method[4].

3. Conclusion Face recognition is a challenging problem in the field of image analysis and computer vision that has received a great deal of attention over the last few years because of its many applications in various domains. Research has been conducted vigorously in this area for the past four decades or so, and though huge progress has been made this application will be developed using Java Technique. This application will help to solve the security issues of the system as well as country.

Acknowledgment First and foremost, we would like to thank my guide, Prof. Ranjana M.Kedar, for his guidance and support. We will forever remain grateful for the constant support and guidance extended by guide, in making this paper. Through our many discussions, she helped us to form and solidify ideas. The invaluable discussions we had with her, the penetrating questions she has put to us and the constant motivation, has all led to the development of this project. We are also thankful to our family members for encouragement and support.

References [1] A. K. Jain, R. Bolle, and S. Pankanti, "Biometrics: Personal Identification in Networked Security," A. K. Jain, R. Bolle, and S. Pankanti, Eds.: Kluwer Academic Publishers, 1999. [2] J. N. K. Liu, M. Wang, and B. Feng, "iBotGuard: an Internet-based intelligent robot security system using invariant face recognition against intruder," IEEE Transactions on Systems Man And Cybernetics Part C-Applications And Reviews, Vol.35, pp.97-105, 2005. [3] H. Moon, "Biometrics Person Authentication Using Projection-Based Face Recognition System in Verification Scenario," in International Conference on Bioinformatics and its Applications. Hong Kong, China, 2004, pp.207-213. [4] P. J. Phillips, H. Moon, P. J. Rauss, and S. A. Rizvi, "The FERET Evaluation Methodology for Face Recognition Algorithms," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.22, pp.1090-1104, 2000. [5] T. Choudhry, B. Clarkson, T. Jebara, and A. Pentland, "Multimodal person recognition using unconstrained audio and video," in Proceedings, International Conference on Audio and Video-Based Person Authentication, 1999, pp.176-181. [6] S. L. Wijaya, M. Savvides, and B. V. K. V. Kumar, "Illumination-tolerant face verification of lowbitrate JPEG2000 wavelet images with advanced correlation filters for handheld devices," Applied Optics, Vol.44, pp.655-665, 2005. Chinmay Gadgil,IJRIT

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[7] E. Acosta, L. Torres, A. Albiol, and E. J. Delp, "An automatic face detection and recognition system for video indexing applications," in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Vol.4. Orlando, Florida, 2002, pp.3644-3647. [8] J.-H. Lee and W.-Y. Kim, "Video Summarization and Retrieval System Using Face Recognition and MPEG-7 Descriptors," in Image and Video Retrieval, Vol.3115, Lecture Notes in Computer Science: Springer Berlin / Heidelberg, 2004, pp.179-188. [9] C. G. Tredoux, Y. Rosenthal, L. d. Costa, and D. Nunez, "Face reconstruction using a configural, eigenface-based composite system," in 3rd Biennial Meeting of the Society for Applied Research in Memory and Cognition (SARMAC). Boulder, Colorado, USA, 1999.

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